similar to: boot and variances of the bootstrap replicates of the variable of interest?

Hello, If you want to bootstrap a statistic, I suggest you use base package boot. You would need the data in a data.frame, see how you could do it. library(boot) bootMedianSE <- function(data, indices){ d <- data[indices, ] fit <- rq(crp ~ bmi + glucose, tau = 0.5, data = d) ypred <- predict(fit) y <- d$crp median(y - ypred)^2 } dat <-

People of R(th), I have been ramming my head against this problem, and I wondered if anyone could lend a hand. I want to parallelize a bootstrap of a linear model on my 8-core mac. Below is the process that I want to parallelize (namely, the m2.ph.rlm.boot<-boot(m2.ph,m2.ph.fun, R = nboot) command). This is an extension of the bootstrapping linear models example in Venables and Ripley to

I forgot, you should also set.seed() before calling boot() to make the results reproducible. Rui Barradas On 5/22/2018 10:00 AM, Rui Barradas wrote: > Hello, > > If you want to bootstrap a statistic, I suggest you use base package boot. > You would need the data in a data.frame, see how you could do it. > > > library(boot) > > bootMedianSE <- function(data,

Hello all, I am re-posting my previous question with a simpler, more transparent, commented code. I have been ramming my head against this problem, and I wondered if anyone could lend a hand. I want to make parallel a bootstrap of a linear mixed model on my 8-core mac. Below is the process that I want to make parallel (namely, the boot.out<-boot(dat.res,boot.fun, R = nboot) command).

Dear R-experts, I am trying to bootstrap (and average) the median squared error evaluation metric for a robust regression. I can't get it. What is going wrong ? Here is the reproducible example. ############################# install.packages( "quantreg" ) library(quantreg) crp <-c(12,14,13,24,25,34,45,56,25,34,47,44,35,24,53,44,55,46,36,67) bmi

I wonder if someone could help me adjusting the following code to parallelized snow code: #Creating a data set (not needed to be parallel) n<-100 p<-100 x<-matrix(rnorm(n*p),p) y<-rnorm(n) # Bootstrapping nboot<-1000 alpha<-0.05 rhoboot <- array(0, dim=c(p,nboot)) bootranks <- array(0, dim=c(p,nboot)) bootsamples <- array( floor(runif(n*nboot)*n+1), dim=c(n,nboot)) for

FIXME:pthread_rwlock_rdlock FIXME:pthread_rwlock_unlock what's this, what's causing it and what needs fixing (I'll give it a look if it's needed) or is it just an old error that dosn't really need fixing (I can run almost everything I want to and the all show this error) just intersted Rob

Dear Friends and Colleagues, I'm puzzling over how to interpret or use some bootstrap intervals. I think that I know what I should do, but I want to check with knowledgeable people first! I'm using a studentized non-parametric bootstrap to estimate 95% confidence intervals for three parameters. I estimate the variance of the bootstrap replicates using another bootstrap. The script

Hi, I am new to clustering and was wondering why pvclust using "maximum" as distance measure nearly always results in p-values above 95%. I wrote an example programme which demonstrates this effect. I uploaded a PDF showing the results Here is the code which produces the PDF file: ------------------------------------------------------------------------------------- s <-

Hello, My question likely got buried so I am reposting it in the hopes that someone has an answer. I have thought more about the question and modified my question. I hope tha my specific question is: I am attempting to create a bootstrap procedure for a finite sample using the theory of Rao and Wu, JASA (1988) that replicates within each strata (h) n_h - 1 times. To this end, I require a

Hi all, I have a problem with getting my code to do what I want! This is the code I have: create.means.one.size<-function(nsample,var,nboot){ mat.x<-matrix(0,nrow=nboot,ncol=nsample) for(i in 1:nboot){ mat.x[i,]<-sample(var,nsample,replace=T) } mean.mat<-rep(0,nboot) for(i in 1:nboot){ mean.mat[i]<-mean(mat.x[i,]) } sd.mean<-sd(mean.mat) return(mean.mat) } where

Hi everybody, I'm trying to analyse a set of data with a non-normal response, 2 fixed effects and 1 nested random effect with strong heteroscedasticity in the model. I planned to use the function lmer : lmer(resp~var1*var2 + (1|rand)) and then use permutations based on the t-statistic given by lmer to get p-values. 1/ Is it a correct way to obtain p-values for my variables ? (see below)

Dear experienced R-users, I am having some probably trivial trouble estimating the confidence interval for the difference of two group means, with groups been of unequal sample size. I am using the "Bootstrap" package and the function "bcanon"(bcanon(x, nboot, theta, ...,alpha=c(0.025, 0.05, 0.1, 0.16, 0.84, 0.9, 0.95, 0.975)) for Nonparametric BCa confidence limits. The

Hi, I'm new to R (and statistics) and my boss has thrown me in the deep-end with the following task: We want to evaluate the impact that sampling size has on our ability to create a robust model, or evaluate how robust the model is to sample size for the purpose of cross-validation i.e. in our current project we have collected a series of independent data at 250 locations, from which

Dear R community, I have currently some problems with non linear regression analysis in R. My data correspond to the degradation kinetic of a pollutant in two different soil A and B, x data are time in day and y data are pollutant concentration in soil. In a first time, I want to fit the data for the soil A by using the Ct = C0*exp(-k*Tpst) with Ct the concentration of pollutant at time t, C0

Hello, &nbsp; I want to sum first three terms of each column of matrix. But I don't calculate with "apply" function. &nbsp; skwkrt&lt;-function(N=10000,mu=0,sigma=1,n=100, nboot=1000,alpha=0.05){ x&lt;-rnorm(N,mu,sigma)#population samplex&lt;-matrix(sample(x,n*nboot,replace=T),nrow=nboot) #... } &nbsp; is that: suppose a is a 5x2 matrix. &nbsp;a={1,2,3,4,5

Full_Name: Charles J. Geyer Version: 1.3.1 OS: linux-gnu-i686 Submission from: (NULL) (134.84.86.22) ltsreg gives incomprehensible (to me) warnings A homework problem for nonparametrics ########## start example ########## library(bootstrap) data(cell) names(cell) attach(cell) library(lqs) plot(V1, V2) fred <- ltsreg(V2 ~ V1 + I(V1^2)) curve(predict(fred, data.frame(V1 = x)), add = TRUE)

Hi all, I'm new to R and I'm struggling to decipher an error message. Briefly, I am trying to use the pvclust package to do hierarchical clustering of some CGH data. The data is from the Progenetix CGH database. It is arranged as a table where each column is a single case and each row is a single chromosome band. The value in each cell is either 0, 1, 2, or -1. Corresponding to no change,

Hi, I am using errorest function from ipred package. I am hoping to perform "bootstrap 0.632+" and "bootstrap leave one out". According to the manual page for errorest, i use the following command: ce632[i]<-errorest(ytrain ~., data=mydata, model=lda, estimator=c("boot","632plus"), predict=mypredict.lda)$error It didn't work. I then tried the

I'm using R 2.15.1 on a 64-bit machine with Windows 7 Home Premium and package 'boot'. I've found that using a number of bootstrap resamples in boot() that is less than the number of data results in a fatal error. Once the number of resamples meets or exceeds the number of data, the error disappears. Sample problem (screwy subscripted syntax is a relic of edited down a more